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RabbitMQ Mcp Go

RabbitMQ Mcp Go

Local
github.com/maiconjobimDeveloper Tools2Updated 68w ago

About RabbitMQ Mcp Go

RabbitMQ MCP Go is a Go implementation of an MCP server for RabbitMQ, intended for LLM applications that publish messages through a standardized interface. It supports text or JSON payloads and optional message headers.

Key features

  1. 01Publish messages to queues or exchanges
  2. 02Support text and JSON content types
  3. 03Pass JSON-formatted message headers

Use cases

  1. 01Send a text notification to a queue
  2. 02Publish a JSON order update to an exchange
  3. 03Send messages with headers such as priority

MCP support

The server exposes a RabbitMQ message-publishing tool to MCP clients and uses RABBITMQ_URL to configure the connection.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

RabbitMQ MCP Go

A Go implementation of Model Control Protocol (MCP) server for RabbitMQ integration.

This server provides an implementation for interacting with RabbitMQ via the MCP protocol, enabling LLM models to perform common RabbitMQ operations through a standardized interface.

Go Report Card Go Version SLSA 3 Go Reference GitHub Release License: MIT

rabbitmq-mcp-demo

Overview

The RabbitMQ MCP Server bridges the gap between LLM models and RabbitMQ, allowing them to:

  • Publish a message to a RabbitMQ queue or exchange

Project Structure

.
├── cmd/                    # Application entry points
├── internal/              # Private application code
│   ├── config/           # Configuration management
│   ├── tools/            # MCP tools implementations
│   ├── prompts/          # Prompt templates and configurations
│   └── resources/        # Shared resources and utilities
├── pkg/                   # Public library code
├── api/                   # API definitions and documentation
└── scripts/              # Build and utility scripts

Directory Overview

Internal Directory Structure
  • tools/: Contains all MCP tool implementations. Each tool is a separate package that implements the MCP tool interface.
  • prompts/: Stores prompt templates and configurations used by the MCP tools.
  • resources/: Houses shared utilities, helper functions, and reusable components.
  • config/: Manages application configuration.

Getting Started

  1. Install Go 1.24.3:
asdf install golang 1.24.3
  1. Clone the repository:
git clone https://github.com/yourusername/rabbitmq-mcp-go.git
cd rabbitmq-mcp-go
  1. Install dependencies:
go mod download
  1. Build the project:
go build ./cmd/...

MCP Client Integration

Basic Configuration

Add this configuration to your MCP client settings:

{
  "mcpServers": {
    "rabbitmq": {
      "command": "rabbitmq-mcp-server",
      "env": {
        "RABBITMQ_URL": "amqp://guest:guest@localhost:5672/"
      }
    }
  }
}
Cursor Integration

To use with Cursor, create or edit ~/.cursor/mcp.json:

{
  "mcpServers": {
    "rabbitmq": {
      "command": "rabbitmq-mcp-server",
      "env": {
        "RABBITMQ_URL": "amqp://guest:guest@localhost:5672/"
      }
    }
  }
}
Claude Desktop Integration

To use with Claude Desktop, edit your configuration file:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json
{
  "mcpServers": {
    "rabbitmq": {
      "command": "rabbitmq-mcp-server",
      "env": {
        "RABBITMQ_URL": "amqp://guest:guest@localhost:5672/"
      }
    }
  }
}

Using the RabbitMQ Publish Tool

The publish tool allows you to send messages to RabbitMQ queues or exchanges through the MCP interface.

Tool Parameters
  • queue (string, optional): Queue name to publish to
  • exchange (string, optional): Exchange name to publish to
  • message (string, required): Message content to publish
  • content_type (string, optional): Content type of the message
    • Supported values: "text/plain" (default), "application/json"
  • headers (string, optional): Message headers in JSON format
Example Interactions

1. Publishing to a Queue:

User: "Send a message 'Hello World' to the queue 'my_queue'"

AI Assistant will use the publish tool:

{
  "queue": "my_queue",
  "message": "Hello, World!",
  "content_type": "text/plain"
}

Response:

"Message published successfully to my_queue"

2. Publishing JSON to an Exchange:

User: "Publish order status update to the 'orders' exchange"

AI Assistant will use the publish tool:

{
  "exchange": "orders",
  "message": "{\"order_id\": \"12345\", \"status\": \"completed\"}",
  "content_type": "application/json"
}

Response:

"Message published successfully to orders"

1. Publishing with Headers:

User: "Send a high-priority message to the notifications queue"

AI Assistant will use the publish tool:

{
  "queue": "notifications",
  "message": "Important system update",
  "headers": "{\"priority\": \"high\", \"timestamp\": \"2024-03-20T12:00:00Z\"}"
}
Error Handling

The tool will return an error in the following cases:

  • Neither queue nor exchange is specified
  • Required message parameter is missing
  • Invalid JSON format when content_type is "application/json"
  • RabbitMQ connection or publishing errors

Development

To add a new MCP tool:

  1. Create a new directory under internal/tools/
  2. Implement the MCP tool interface
  3. Register the tool in the main application
  4. Add corresponding prompts in internal/prompts/ if needed
  5. Document the tool in the tools README
Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.